Bioinspired Thermo-Responsive Xyloglucan-Cellulose Nanocrystal Hydrogels
Bibliographic record
Abstract
Thermo-responsive hydrogels present unique properties, such as tunable mechanical performance or changes in volume, which make them attractive for applications including wound healing dressings, drug delivery vehicles, and implants, among others. This work reports the implementation of bio-based thermo-responsive hydrogels comprised of xyloglucan (XG) and cellulose nanocrystals (CNCs). Thermo-responsive properties were obtained by enzymatic degalactosylation of tamarind seed XG (DG-XG), which reduced the galactose residue content by ~50%, and imparted a reversible thermal transition. XG with comparable molar mass to DG-XG was achieved by ultrasonication treatment (XGu) for direct comparison of behavior. The hydrogels were prepared by simple mixing of DG-XG or XGu with CNCs in water. Phase diagrams were established to identify the ratios of DG-XG or XGu to CNCs (from 1:300 to 20:1 by mass) that yielded a viscous liquid, a phase separated mixture, a simple gel, or a thermo-responsive gel. Gelation occurred at a DG-XG or XGu to CNC ratio higher than that needed for the full surface coverage of CNCs, and required relatively high overall concentrations of both components (tested concentrations up to 20 g/L XG and 30 g/L CNCs). This is likely a result of the increase in effective hydrodynamic volume of CNCs due to the formation of XG-CNC complexes. Investigation of the adsorption behavior indicated that DG-XG formed a more rigid layer on CNCs compared to XGu. Rheological properties of the hydrogels were characterized and a reversible thermal transition was found for DG-XG/CNC gels at 35°C, where the mechanical properties of the gel could be tuned by adjusting the CNC content
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".